Textual entailment

ABSTRACT

Examples of a textual entailment generation system are provided. The system obtains a query from a user and implements an artificial intelligence component to identify a premise, a word index, and a premise index associated with the query. The system may implement a first cognitive learning operation to determine a plurality of hypothesis and a hypothesis index corresponding to the premise. The system may generate a confidence index for each of the plurality of hypothesis based on a comparison of the hypothesis index with the premise index. The system may determine an entailment value, a contradiction value, and a neutral entailment value based on the confidence index for each of the plurality of hypothesis. The system may generate an entailment result relevant for resolving the query comprising the plurality of hypothesis along with the corresponding entailed output index.

BACKGROUND

Textual entailment relates to a directional relationship between textfragments in a text document, based on a natural language processingoperation. The directional relationship in textual entailment may bebased on mimicking cognitive comprehension of a human being. Forexample, the directional relationship may hold whenever the truth of onetext fragment follows from another text fragment.

Many approaches have been considered for textual entailment. Theseapproaches include, for example, word embedding, logical models,graphical models, rule systems, contextual focusing, evaluating asurface syntax, evaluation lexical relationships, and machine learning.These approaches may be based on a natural language understanding andmay suffer from multi-dimensional characteristics of a natural language.For example, a characteristic of natural language may be that there arevarious ways of interpreting a single text and that the same meaning maybe implied by different texts. Such variability of semantic expressionmay be due to language ambiguity, which may result in amulti-directional mapping between language expressions and meanings. Thetask of textual entailment may involve recognizing when two texts havethe same meaning and creating a similar or shorter text that may expressalmost the same information.

Various methods that may presently be used for textual entailment mayestablish a unidirectional relationship between language expressions andmeanings. Additionally, mathematical solutions to establish textualentailment may be based on the directional property of thisunidirectional relationship, by making a comparison between directionalsimilarities of the texts involved. Such an approach may rendercurrently available mathematical solutions ineffective in dealing withthe multi-dimensional characteristic of a natural language.

Therefore, to ensure efficiency and completeness, an entailmenttechnique may be required to ensure that a multi-directionalrelationship may be established between language expressions andcorresponding interpretations. There is a need for an entailment system,which may transform the entailment operations into an insight-drivenentailment function. Further, it may be required to adaptively generatea hypothesis from a text fragment from a given text document and testthe hypothesis for being positively, negatively, or neutrallyconditioned onto the given text. Additionally, there may be arequirement for using an analytics centric approach for gatheringinsights from a document using entailment operations.

Accordingly, a technical problem with the currently available systemsfor generation of textual entailment is that they may be inefficient,inaccurate, and/or not scalable. There is a need for a textualentailment system that may account for the various factors mentionedabove, amongst others, for multi-dimensional relationships betweenvarious text fragments from a text document in an efficient, andcost-effective manner.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a diagram for a textual entailment system, accordingto an example embodiment of the present disclosure.

FIG. 2 illustrates various components of a textual entailment system,according to an example embodiment of the present disclosure.

FIG. 3 illustrates a network architectural diagram for generatingtextual entailment using a textual entailment system, according to anexample embodiment of the present disclosure.

FIG. 4 illustrates a flow diagram for a configuration of a Kerasembedding layer using a word index embedding for deployment of a textualentailment system, according to an example embodiment of the presentdisclosure.

FIG. 5 illustrates a flow diagram for initializing an array of indicescorresponding to a sentence of words in a word index using a textualentailment system, according to an example embodiment of the presentdisclosure.

FIG. 6A illustrates a pictorial representation of a premise mapping witha hypothesis using a textual entailment system, according to an exampleembodiment of the present disclosure.

FIG. 6B illustrates a pictorial representation of a premise mapping witha hypothesis using a textual entailment system, according to an exampleembodiment of the present disclosure.

FIG. 6C illustrates a pictorial representation of a premise mapping witha hypothesis using a textual entailment system, according to an exampleembodiment of the present disclosure.

FIG. 6D illustrates a pictorial representation of a premise mapping witha hypothesis using a textual entailment system, according to an exampleembodiment of the present disclosure.

FIG. 7A illustrates a flow diagram for a tokenization operation,according to an example embodiment of the present disclosure.

FIG. 7B illustrates a flow diagram for a prediction of an entailmentusing a textual entailment system, according to an example embodiment ofthe present disclosure.

FIG. 8 illustrates a hardware platform for the implementation of atextual entailment system, according to an example embodiment of thepresent disclosure.

FIGS. 9A and 9B illustrate a process flowchart for a textual entailmentsystem, according to an example embodiment of the present disclosure.

DETAILED DESCRIPTION

For simplicity and illustrative purposes, the present disclosure isdescribed by referring mainly to examples thereof. The examples of thepresent disclosure described herein may be used together in differentcombinations. In the following description, details are set forth inorder to provide an understanding of the present disclosure. It will bereadily apparent, however, that the present disclosure may be practicedwithout limitation to all these details. Also, throughout the presentdisclosure, the terms “a” and “an” are intended to denote at least oneof a particular element. The terms “a” and “an” may also denote morethan one of a particular element. As used herein, the term “includes”means includes but not limited to, the term “including” means includingbut not limited to. The term “based on” means based at least in part on,the term “based upon” means based at least in part upon, and the term“such as” means such as but not limited to. The term “relevant” meansclosely connected or appropriate to what is being done or considered.

The present disclosure describes systems and methods for a textualentailment including a textual entailment system. The textual entailmentsystem (referred to as “system” hereinafter) may be used to generateentailment inferences from a given text. The entailment generation mayhave applications in a variety of industry domains, such as, forexample, healthcare, finance, and technology (web search),pharmacovigilance and the like. The entailment may capture semanticreasoning abilities used in a broad set of applications like questionanswering, information retrieval, information extraction, textsummarization, and machine comprehension. The system may generate aplurality of hypothesis for a given text. The system may deploy athree-dimensional approach for recognizing textual entailment (RTE). TheRTE may be the ability to determine if a hypothesis entails a giventext. The system may generate a multi-directional between a text and theplurality of hypothesis. In an example, the system may generate anentailment relationship, a contradiction relationship, and a neutralityrelationship between a text and the plurality of hypothesis. The systemmay deploy various word vector models for RTE. In accordance withvarious embodiments of the present disclosure, the system may be aneural network model for deriving textual entailment using word vectorsfrom various models, for example, Word2vec® and Gensim®. The system mayprovide an improvement in entailment classification using deep learningnetworks to ingest natural language text using random initialization totrain a set of embeddings as well as pre-trained embeddings.

The system may include a processor, an entailment data organizer, ahypothesis generator, and a modeler. The processor may be coupled to theentailment data organizer, the hypothesis generator, and the modeler.The entailment data organizer may obtain a query from a user. The querymay be indicating a data entailment requirement comprising entailmentdata and associated with entailment operations. The entailment dataorganizer may implement an artificial intelligence component to identifya word index from a knowledge database. The word index maybe including aplurality of words being associated with the data entailmentrequirement. The entailment data organizer may implement an artificialintelligence component to identify a premise from the entailment data.The premise may be comprising a first word data set associated with thedata entailment requirement. The entailment data organizer may implementan artificial intelligence component to determine a premise index bymapping the first word data set with the word index.

The hypothesis generator may implement a first cognitive learningoperation to determine a plurality of hypothesis corresponding to thepremise. In accordance with various embodiments of the presentdisclosure, each of the plurality of hypothesis may be comprising asecond-word data set and indicating an inference associated with thepremise. The second-word data set may be associated with the word index.The hypothesis generator may determine a hypothesis index by mapping thesecond-word data set with the word index. The hypothesis generator maygenerate a confidence index for each of the plurality of hypothesisbased on a comparison of the hypothesis index with the premise index.

The modeler implement may a second cognitive learning operation todetermine an entailment value based on the confidence index for each ofthe plurality of hypothesis. The entailment value may be indicating aprobability of a hypothesis from the plurality of hypothesis beingpositively associated with the premise. The modeler implement may thesecond cognitive learning operation to determine a contradiction valuefrom the confidence index for each of the plurality of hypothesis. Thecontradiction value may be indicating a probability of a hypothesis fromthe plurality of hypothesis being negatively associated with thepremise. The modeler implement may the second cognitive learningoperation to determine a neutral entailment value from the confidenceindex for each of the plurality of hypothesis. The neutral entailmentvalue indicating a probability of a hypothesis from the plurality ofhypothesis being neutrally associated with the premise. The modelerimplement may the second cognitive learning operation to determine anentailed output index by collating the entailment value, thecontradiction value, and the neutral entailment value for each of theplurality of hypothesis. The modeler implement may the second cognitivelearning operation to generate an entailment result relevant forresolving the query. The entailment result may be comprising theplurality of hypothesis along with the corresponding entailed outputindex.

The embodiments for the data entailment requirement presented herein areexemplary in nature and should be treated as such. For the sake ofbrevity and technical clarity, the description of the textual entailmentsystem may be restricted to few exemplary embodiments, however, to aperson skilled in the art it should be clear that the system may be usedfor the fulfillment of various textual insight generations and dataentailment requirements other than those mentioned hereinafter.

Accordingly, the present disclosure aims to provide a textual entailmentsystem that may account for the various factors mentioned above, amongstothers, to multi-dimensional relationships between various textfragments from a text document in an efficient, and cost-effectivemanner. Furthermore, the present disclosure may categorically analyzevarious parameters that may have an impact on deciding an appropriateentailment relationship amongst various text fragments from a given textdocument.

FIG. 1 illustrates a system for textual entailment system 110 (referredto as system 110 hereinafter), according to an example implementation ofthe present disclosure. In an example, the system 110 may include aprocessor 120. The processor 120 may be coupled to an entailment dataorganizer 130, a hypothesis generator 140 and a modeler 150.

In accordance with an embodiment of the present disclosure. Theentailment data organizer 130 may obtain a query from a user. The querymay be indicating a data entailment requirement comprising entailmentdata and associated with entailment operations. The entailment data maybe a text document provided by a user to the system. The data entailmentrequirement may be associated with at least one of a process, anorganization, and an industry-relevant for entailment operations. In anexample, the data entailment requirement may indicate a requirement,which may refer to a purpose of generating insights from a text documentin an automated manner. For example, the purpose may be to monitor theeffects of the drugs after they may be licensed for use, in order toidentify and evaluate previously unreported adverse events/reactions. Inan example, adverse events related to a drug may be reported to drugmakers by, for example, a doctor as a medical literature document. Themedical literature document may be a complex document describing casedetails and various related entities. It may contain patient demographicdetails, medical history, description of the adverse events and themedications used, other related medical cases, and the like. In additionto identifying various entities it may also be important to identify orestablish the relationship between various key entities.

The textual entailment may facilitate in providing an answer to keyquestions about a drug's role in a case or whether there is a causalrelationship between a suspect drug and adverse event from the medicalliterature. The purpose of the data entailment requirement may be tounderstand and evaluate possible demographic regions or a geographicallocation by an organization for augmenting understanding regardingmarket requirements so as to adopt a more insight-driven approachtowards sales and marketing. The purpose of the data entailmentrequirement may be to analyze various finance dossiers for generatinginsights related to various financial operations. The purpose of thedata entailment requirement may be to capture semantic reasoningabilities, which may be used in a broad set of applications likequestion answering, information retrieval, information extraction, textsummarization, and machine comprehension. The embodiments for theprospect assessment requirements presented herein are exemplary innature and should be treated as such.

The entailment data organizer 130 may implement an artificialintelligence component to identify a word index from a knowledgedatabase. In accordance with various embodiments of the presentdisclosure, the artificial intelligence component may include artificialintelligence techniques, for example, a Natural Language Processing(NLP) model. In an example, the NLP model may be developed using theLanguage Understanding Intelligent Service (LUIS). The NLP applicationmay be development of a neural network with an attention model(described in subsequent paragraphs) using a Keras library foridentifying sentences from a text document. The Keras library may referto the Keras library written in Python™ and capable of running on top ofTensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. The Keraslibrary may be designed to enable development of deep neural networks.In an example, a set of Keras models may be developed using randominitialization and an embedding layer as described by way of FIG. 3. Inaccordance with various embodiments of the present disclosure, theembedding layer may include a Keras embedding layer, a GloVe wordembedding layer. For sake of brevity and technical clarity furtherdetails regarding the Keras library may be not be described herein,however, the same should be clear to a person skilled in the art.

The word index may be including a plurality of words being associatedwith the data entailment requirement. In an example, the knowledgedatabase may be a natural language data directory. The knowledgedatabase may be a pre-existing text corpus stored in the system 110. Thetext corpus may refer to a large and structured set of texts that may beused to do a statistical analysis, hypothesis testing, checkingoccurrences or validating linguistic rules within a specific languageterritory. In an example, the text corpus may be the Stanford NaturalLanguage Inference (SNLI) text corpus comprising a collection of labeledsentence pairs. For the sake of brevity and technical clarity, detailsabout the SNLI have not been mentioned herein but should be clear to aperson skilled in the art. The SNLI text corpus may be used to determinean entailment, a contradiction, and a piece of neutral information for athree-way task challenge on 570,000 labeled sentence pairs. Theartificial intelligence component may map a set of words from the dataentailment requirement with the text corpus to identify the words index.The word index may include the plurality of words, which may be presentin the data entailment requirement and may be present as labeledsentence pairs in the text corpus.

The entailment data organizer 130 may implement an artificialintelligence component to identify a premise from the entailment data.As mentioned above, the entailment data may be a text document providedby a user to the system. In an example, the entailment data may includemedical literature related to a medical product, medical records relatedto various patients suffering from a medical condition,pharmacovigilance agreements, various customer interactions, and productdossiers. In accordance with various embodiments of the presentdisclosure, the entailment data may be in a portable document format(pdf), a doc/docx format, a txt format, a text from webscraper format, arich text format (RTF), a Hypertext Markup Language (HTML) format. Forsake of brevity and technical clarity, other formats of the entailmentdata have not been mentioned herein, by should be clear to a personskilled in the art. The premise may be comprising a first word data setassociated with the data entailment requirement. In an example, thepremise may be a text fragment segmented from the entailment data by theentailment data organizer 130. The premise may be the text fragment fromthe entailment data, which may be identified by the artificialintelligence component for generating an entailment insight. In anexample, the entailment data organizer 130 may identify multiplepremises for a given text document.

The first word data set included in the premise may be a set of wordsthat may form the text fragment, which may be identified as the premiseby the artificial intelligence component. In an example, the premiseidentified from the entailment data comprising a text document may be“treatment for angina pectoris with dipyridamole 220 mg daily andverapamil 120 mg daily had been started three months and two weekspreviously, respectively”. The first word data set for the premise mayinclude words such as “treatment”, “for”, “angina”, “pectoris”, “with”,“dipyridamole”, “220”, “mg”, “daily”, “and”, “verapamil”, “120”, “mg”,“daily”, “had”, “been”, “started”, “three”, “months”, “and”, “two”,“weeks”, “previously”, “respectively”. In an example, the premise may be“on 20 May 2016, the patient started therapy with Provas (valsartan),tablet, at 80 mg once daily via oral route for hypertension. On 20 Mar.2018, the patient was hospitalized for Myocardial infarction andcongestive cardiac failure. The patient was died on the same day due toMyocardial infarction”. The first word data set the premise may includewords such as “patient”, “started”, “therapy”, “with”, “Proves”,“valsartan”, “tablet”, “at”, “80”, “mg”, “once”, “daily”, “via”, “oral”,“route”, “for”, “hypertension”, “20 May 2016”, “20 Mar. 2018”,“patient”, “was”, “hospitalized”, “Myocardial”, “infarction”, “and”,“congestive”, “cardiac”, “failure”.

The entailment data organizer 130 may implement an artificialintelligence component to determine a premise index by mapping the firstword data set with the word index. In an example, the premise index mayinclude a two-dimensional mapping of the first word data set with theword index. The premise index may enable the system 110 to automaticallyinterpret the plurality of words present in the premise for insightgeneration. As mentioned above, the word index may be derived from theknowledge database that may include the text corpus with pre-existingstructured set of texts and linguistic rules. A mapping of the wordsindex with the first word data set may be lead to interpretation of thefirst word data set. In an example, the attention model (mentionedabove) may identify a premise from the entailment data and turn theminto a matrix wherein, the first set of words from the plurality ofwords from the premise may form a column, and the words from the wordindex may form a row. The attention model may make matches between therow and the column for identifying relevant context.

The hypothesis generator 140 may implement a first cognitive learningoperation to determine a plurality of hypothesis corresponding to thepremise. The first cognitive learning operation may include implementingvarious machine learning techniques such as word embedding algorithmslike Glove, Keras embedding algorithm and the like. In an example,various machine learning algorithms such as TensorFlow, SpaCy, PyTorchand the like may be used for deriving a decomposable attention model. Inan example, the decomposable attention model may be a recurrent NeuralNetwork-based attention model. The Recurrent Neural Network (RNN) may bea type of Neural Network where the output from a previous step may befed as input to a current step. The RNN may be deployed to predict thenext word of a sentence, the previous words are required and hence theremay be a need to remember the previous words. The system 110 may deployvarious RNN based models as part of the first cognitive learningoperation for generation of the plurality of hypothesis corresponding tothe premise. In an example, the hypothesis generator 140 may identifythe plurality of hypothesis corresponding to each of the premisesidentified by the entailment data organizer 130 for a given entailmentdata.

In accordance with various embodiments of the present disclosure, eachof the plurality of hypothesis may be comprising a second-word data setand indicating an inference associated with the premise. The inferencemay indicate an implication, which may be derived by the hypothesisgenerator 140 based on the context derived through the premise index. Asmentioned above, the premise index may facilitate generation of thecontext for the premise by mapping the first word data set with wordindex. The hypothesis generator 140 may deploy the context of thepremise to generate the plurality of hypothesis corresponding to thepremise. Each of the plurality of hypothesis may include the second-worddata set. The second-word data set may be associated with the wordindex. As mentioned above, the word index may a pre-existing text corpuswith labeled sentence pairs and linguistic rules. The second-word dataset may be a set of words identified by the hypothesis generator 140from the word index based on the context of the premise as identified bythe premise index to convey an implication of the context of thepremise. The hypothesis generator 140 may determine a hypothesis indexby mapping the second-word data set with the word index. In an example,the hypothesis index may include a two-dimensional mapping of thesecond-word data set with the word index (explained in detail by way ofsubsequent Figs.). The hypothesis index may enable the system 110 toautomatically interpret the second-word data set present in thehypothesis for entailment generation.

The hypothesis generator 140 may generate a confidence index for each ofthe plurality of hypothesis based on a comparison of the hypothesisindex with the premise index. As mentioned above, the premise index maycompare the first data set with the word index and the hypothesis indexmay compare the second-word data set with the word index, wherein theword index may be a pre-existing text corpus with a defined context.Therefore, a comparison between the premise index and the hypothesisindex may provide a comparison between a context as derived from thepremise in regard to a context as derived from the hypothesis. Such acomparison may facilitate the generation of the confidence index for thehypothesis. The hypothesis generator 140 may allocate a high confidenceindex to the hypothesis that may have a context closely matching thecontext of the premise. The hypothesis generator 140 may allocate a lowconfidence index to the hypothesis that may have a context not matchingthe context of the premise. In an example, the hypothesis generator 140may deploy a T-distributed Stochastic Neighbor Embedding (t-SNE)technique wherein similar objects are modeled by nearby points anddissimilar objects are modeled by distant points with high probability.

In accordance with various embodiments of the present disclosure, thehypothesis generator 140 may generate a premise graph and a hypothesisgraph. The premise graph may be mapping the first word data set againstthe second-word data set, and the hypothesis graph mapping thesecond-word data set against the first word data set. As mentionedabove, the premise index may compare the first data set with the wordindex and the hypothesis index may compare the second-word data set withthe word index, wherein the word index may be a pre-existing text corpuswith a defined context. The hypothesis generator 140 may map a set ofwords from the first word data set with a set of words from thesecond-word data set to derive a relationship between the words presentin the premise with regard to words present in the hypothesis in form ofthe premise graph. The premise graph may facilitate in establishing arelationship between the premise and a hypothesis from the plurality ofhypothesis. The hypothesis generator 140 may evaluate the relationshipbetween the premise and a hypothesis from the plurality of hypothesisfor allocating the confidence index to the hypothesis. In an example,the hypothesis generator 140 may deploy a T-distributed StochasticNeighbor Embedding (t-SNE) technique for evaluating the premise graph.The hypothesis generator 140 may map a set of words from the second-worddata set with a set of words from the first word data set to derive arelationship between the words present in a hypothesis from theplurality of hypothesis with regard to words present in the premise inform of the hypothesis graph. The hypothesis generator 140 may evaluatethe relationship between the words present in a hypothesis from theplurality of hypothesis with regard to words present in the premise forallocating the confidence index to the hypothesis. In an example, thehypothesis generator 140 may deploy a T-distributed Stochastic NeighborEmbedding (t-SNE) technique for evaluating the hypothesis graph. In anexample, the hypothesis generator 140 may compare the premise graph andthe hypothesis graph for a hypothesis for allocating the confidenceindex to the hypothesis with regard to the premise (explained in detailby way of FIG. 6).

The modeler 150 implement may a second cognitive learning operation todetermine an entailment value based on the confidence index for each ofthe plurality of hypothesis. In an example, the second cognitivelearning operation may be the NLP model deploying various word embeddingtechniques, for example, Word2vec® and Glove® vectors to train a neuralnetwork to determine if a hypothesis entails a text. In an example, along short-term memory (LSTM) model may be used to determine anentailment value (explained in detail by the way of subsequent Figs.).In an example, a Bi-directional Long Short-Term Memory (BiLSTM) modelmay be used to determine an entailment value (explained in detail by theway of subsequent Figs.). In an example, a Keras Embedding layer may beused to train the system 110. In an example, the Keras Embedding layermay be initialized using the Glove® word embeddings. The premise indexand the hypothesis index may be concatenated to input to the Kerasnetwork. The BiLSTM may be deployed to return sequence values as dottedto obtain an attention layer. The sequence values may be fed as an inputto a stack of three 400D® Dense layers. The output may be flattened andfed to a softmax® bottom layer to derive the entailment value (explainedin detail by the way of subsequent Figs.). The entailment value may beindicating a probability of a hypothesis from the plurality ofhypothesis being positively associated with the premise (explained indetail by way of FIG. 2).

The modeler 150 implement may the second cognitive learning operation todetermine a contradiction value from the confidence index for each ofthe plurality of hypothesis. The contradiction value may be indicating aprobability of a hypothesis from the plurality of hypothesis beingnegatively associated with the premise (explained in detail by way ofFIG. 2). The modeler 150 implement may the second cognitive learningoperation to determine a neutral entailment value from the confidenceindex for each of the plurality of hypothesis. The neutral entailmentvalue indicating a probability of a hypothesis from the plurality ofhypothesis being neutrally associated with the premise (explained indetail by way of FIG. 2). The modeler 150 may deploy the same techniquesfor derivation of the contradiction value and the neutral entailmentvalue as has been described for the derivation of the entailment value.

The modeler 150 implement may the second cognitive learning operation todetermine an entailed output index by collating the entailment value,the contradiction value, and the neutral entailment value for each ofthe plurality of hypothesis. The modeler 150 may determine theentailment value, the contradiction value, and the neutral entailmentvalue for each of the plurality of hypothesis and compare the same. Inan example, the modeler 150 may implement the second cognitive learningoperation for identifying the highest value amongst the entailmentvalue, the contradiction value, and the neutral entailment value foreach of the plurality of hypothesis. For example, entailment value for ahypothesis may be “94.8”, the contradiction value for a hypothesis maybe “0”, and the neutral entailment value may be “4.1”. The modeler 150may present all three of the entailment value, the contradiction value,and the neutral entailment value to a user of the system 110. In anexample, the modeler 150 may compare all three of the entailment value,the contradiction value, and the neutral entailment value and presentthe highest value to a user of the system 110. The modeler 150 implementmay the second cognitive learning operation to generate an entailmentresult relevant for resolving the query. The entailment result may becomprising the plurality of hypothesis along with the correspondingentailed output index. In an example, the entailment result may furtherinclude an entailment output corresponding to the highest value from theentailed output index associated with each of the plurality ofhypothesis (explained in detail by way of FIG. 2).

In accordance with various embodiments of the present disclosure, theentailment data organizer 130 may further establish an entailment datalibrary by associating entailment data with the premise, the pluralityof hypothesis and the confidence index for each of the plurality ofhypothesis. The system 110 may deploy inputs from the entailment datalibrary for improving the efficiency of the first cognitive learningoperation and the second cognitive learning operation.

The embodiments for the artificial intelligence component, the firstcognitive learning operations, and the second cognitive learningoperations presented herein are exemplary in nature and should betreated as such. For the sake of brevity and technical clarity, thedescription of the textual entailment system may be restricted to fewexemplary embodiments, however, to a person skilled in the art it shouldbe clear that the system may be used for the fulfillment of varioustextual entailment requirements other than those mentioned hereinafter.

FIG. 2 illustrates various components of the textual entailment system110, according to an example embodiment of the present disclosure. In anexample, the system 110 may include the processor 120. The processor 120may be coupled to the entailment data organizer 130, the hypothesisgenerator 140 and the modeler 150.

In accordance with an embodiment of the present disclosure. Theentailment data organizer 130 may obtain a query from a user. The querymay be indicating a data entailment requirement 202 comprisingentailment data and associated with entailment operations. Theentailment data may be a text document provided by a user to the system.The data entailment requirement 202 may be associated with at least oneof a process, an organization, and an industry-relevant for entailmentoperations. In an example, the data entailment requirement 202 mayindicate a requirement, which may refer to a purpose of generatinginsights from a text document in an automated manner. For example, thepurpose may be to monitor the effects of the drugs after they may belicensed for use, in order to identify and evaluate previouslyunreported adverse events/reactions. In an example, adverse eventsrelated to a drug may be reported to drug makers by, for example, adoctor as a medical literature document. The medical literature documentmay be a complex document describing case details and various relatedentities. It may contain patient demographic details, medical history,description of the adverse events and the medications used, otherrelated medical cases, and the like. In addition to identifying variousentities it may also be important to identify or establish therelationship between various key entities. The textual entailment mayfacilitate in providing an answer to key questions about a drug's rolein a case or whether there is a causal relationship between a suspectdrug and adverse event from the medical literature. The purpose of thedata entailment requirement 202 may be to understand and evaluatepossible demographic regions or a geographical location by anorganization for augmenting understanding regarding market requirementsso as to adopt a more insight-driven approach towards sales andmarketing. The purpose of the data entailment requirement 202 may be toanalyze various finance dossiers for generating insights related tovarious financial operations. The purpose of the data entailmentrequirement 202 may be to capture semantic reasoning abilities, whichmay be used in a broad set of applications like question answering,information retrieval, information extraction, text summarization, andmachine comprehension. The embodiments for the prospect assessmentrequirements presented herein are exemplary in nature and should betreated as such.

The entailment data organizer 130 may implement an artificialintelligence component 218 to identify a word index 204 from a knowledgedatabase 206. In accordance with various embodiments of the presentdisclosure, the artificial intelligence component 218 may includeartificial intelligence techniques, for example, a Natural LanguageProcessing (NLP) model. In an example, the NLP model may be developedusing the Language Understanding Intelligent Service (LUIS). The NLPapplication may be development of a neural network with an attentionmodel (described in subsequent paragraphs) using a Keras library foridentifying sentences from a text document. The Keras library may referto the Keras library written in Python™ and capable of running on top ofTensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. The Keraslibrary may be designed to enable development of deep neural networks.In an example, a set of Keras models may be developed using randominitialization and an embedding layer as described by way of FIG. 3. Inaccordance with various embodiments of the present disclosure, theembedding layer may include a Keras embedding layer, a GloVe wordembedding layer. For sake of brevity and technical clarity furtherdetails regarding the Keras library may be not be described herein,however, the same should be clear to a person skilled in the art.

The word index 204 may be including a plurality of words 208 beingassociated with the data entailment requirement 202. In an example, theknowledge database 206 may be a natural language data directory. Theknowledge database 206 may be a pre-existing text corpus stored in thesystem 110. The text corpus may refer to a large and structured set oftexts that may be used to do a statistical analysis, hypothesis testing,checking occurrences or validating linguistic rules within a specificlanguage territory. In an example, the text corpus may be the StanfordNatural Language Inference (SNLI) text corpus comprising a collection oflabeled sentence pairs. For the sake of brevity and technical clarity,details about the SNLI have not been mentioned herein but should beclear to a person skilled in the art. The SNLI text corpus may be usedto determine an entailment, a contradiction, and a piece of neutralinformation for a three-way task challenge on 570,000 labeled sentencepairs. The artificial intelligence component 218 may map a set of wordsfrom the data entailment requirement 202 with the text corpus toidentify the words index. The word index 204 may include the pluralityof words 208, which may be present in the data entailment requirement202 and may be present as labeled sentence pairs in the text corpus.

The entailment data organizer 130 may implement an artificialintelligence component 218 to identify a premise 212 from the entailmentdata. As mentioned above, the entailment data may be a text documentprovided by a user to the system. In an example, the entailment data mayinclude medical literature related to a medical product, medical recordsrelated to various patients suffering from a medical condition,pharmacovigilance agreements, various customer interactions, and productdossiers. In accordance with various embodiments of the presentdisclosure, the entailment data may be in a portable document format(pdf), a doc/docx format, a txt format, a text from webscraper format, arich text format (RTF), and a hypertext markup language (HTML) format.For sake of brevity and technical clarity, other formats of theentailment data have not been mentioned herein, by should be clear to aperson skilled in the art. The premise 212 may be comprising a firstword data set 214 associated with the data entailment requirement 202.In an example, the premise 212 may be a text fragment segmented from theentailment data by the entailment data organizer 130. The premise 212may be the text fragment from the entailment data, which may beidentified by the artificial intelligence component 218 for generatingan entailment insight. In an example, the entailment data organizer 130may identify multiple premises 212 for a given text document. Inaccordance with various embodiments of the present disclosure, thepremise 212 may be generated using a tokenization operation (explainedin detail by way of FIGS. 7A and 7B). The tokenization operation mayrefer to a process of dividing text into a set of meaningful pieces. Thepremise 212 may be referred to as a token, which may be an instance of asequence of characters in a particular document that may be groupedtogether as a useful semantic unit for processing.

The first word data set 214 included in the premise 212 may be a set ofwords that may form the text fragment, which may be identified as thepremise 212 by the artificial intelligence component 218. In an example,the premise 212 identified from the entailment data comprising a textdocument may be “treatment for angina pectoris with dipyridamole 220 mgdaily and verapamil 120 mg daily had been started three months and twoweeks previously, respectively”. The first word data set 214 for thepremise 212 may include words such as “treatment”, “for”, “angina”,“pectoris”, “with”, “dipyridamole”, “220”, “mg”, “daily”, “and”,“verapamil”, “120”, “mg”, “daily”, “had”, “been”, “started”, “three”,“months”, “and”, “two”, “weeks”, “previously”, “respectively”. In anexample, the premise 212 may be “on 20 May 2016, the patient startedtherapy with Provas (valsartan), tablet, at 80 mg once daily via oralroute for hypertension. On 20 Mar. 2018, the patient was hospitalizedfor Myocardial infarction and congestive cardiac failure. The patientwas died on the same day due to Myocardial infarction”. The first worddata set 214 the premise 212 may include words such as “patient”,“started”, “therapy”, “with”, “Provas”, “valsartan”, “tablet”, “at”,“80”, “mg”, “once”, “daily”, “via”, “oral”, “route”, “for”,“hypertension”, “20 May 2016”, “20 Mar. 2018”, “patient”, “was”,“hospitalized”, “Myocardial”, “infarction”, “and”, “congestive”,“cardiac”, “failure”.

The entailment data organizer 130 may implement the artificialintelligence component 218 to determine a premise index 216 by mappingthe first word data set 214 with the word index 204. In an example, thepremise index 216 may include a two-dimensional mapping of the firstword data set 214 with the word index 204. The premise index 216 mayenable the system 110 to automatically interpret the plurality of words208 present in the premise 212 for insight generation. As mentionedabove, the word index 204 may be derived from the knowledge database 206that may include the text corpus with pre-existing structured set oftexts and linguistic rules. A mapping of the words index with the firstword data set 214 may be lead to interpretation of the first word dataset 214. In an example, the attention model (mentioned above) mayidentify the premise 212 from the entailment data and turn them into amatrix wherein, the first set of words from the plurality of words 208from the premise 212 may form a column, and the words from the wordindex 204 may form a row. The attention model may make matches betweenthe row and the column for identifying relevant context. In accordancewith various embodiments of the present disclosure, the entailment dataorganizer 130 may implement a word embedding model as an artificialintelligence technique for mapping the words index with the first worddata set 214. The system 110 may deploy a Keras‘pretrained_embedding_layer’ (explained in detail by way of FIG. 4)using Glove word embeddings for mapping the words index with the firstword data set 214.

The hypothesis generator 140 may implement a first cognitive learningoperation 220 to determine a plurality of hypothesis 222 correspondingto the premise 212. The first cognitive learning operation 220 mayinclude implementing various machine learning techniques such as wordembedding algorithms like GloVe, Keras embedding algorithm and the like.In an example, various machine learning algorithms such as TensorFlow,SpaCy, PyTorch and the like may be used for deriving a decomposableattention model. In an example, the decomposable attention model may bea recurrent Neural Network-based attention model. The Recurrent NeuralNetwork (RNN) may be a type of Neural Network where the output from aprevious step may be fed as input to a current step. The RNN may bedeployed to predict the next word of a sentence, the previous words arerequired and hence there may be a need to remember the previous words.The system 110 may deploy various RNN based models as part of the firstcognitive learning operation 220 for generation of the plurality ofhypothesis 222 corresponding to the premise 212. In an example, thehypothesis generator 140 may identify the plurality of hypothesis 222corresponding to each of the premise 212 identified by the entailmentdata organizer 130 for a given entailment data.

In accordance with various embodiments of the present disclosure, eachof the plurality of hypothesis 222 may be comprising a second-word dataset 224 and indicating an inference associated with the premise 212. Theinference may indicate an implication, which may be derived by thehypothesis generator 140 based on the context derived through thepremise index 216. As mentioned above, the premise index 216 mayfacilitate generation of the context for the premise 212 by mapping thefirst word data set 214 with word index 204. The hypothesis generator140 may deploy the context of the premise 212 to generate the pluralityof hypothesis 222 corresponding to the premise 212. Each of theplurality of hypothesis 222 may include the second-word data set 224.The second-word data set 224 may be associated with the word index 204.As mentioned above, the word index 204 may a pre-existing text corpuswith labeled sentence pairs and linguistic rules. The second-word dataset 224 may be a set of words identified by the hypothesis generator 140from the word index 204 based on the context of the premise 212 asidentified by the premise index 216 to convey an implication of thecontext of the premise 212. The hypothesis generator 140 may determine ahypothesis index 226 by mapping the second-word data set 224 with theword index 204. In an example, the hypothesis index 226 may include atwo-dimensional mapping of the second-word data set 224 with the wordindex 204 (explained in detail by way of subsequent Figs.). Thehypothesis index 226 may enable the system 110 to automaticallyinterpret the second-word data set 224 present in the hypothesis forentailment generation.

The hypothesis generator 140 may generate a confidence index 228 foreach of the plurality of hypothesis 222 based on a comparison of thehypothesis index 226 with the premise index 216. As mentioned above, thepremise index 216 may compare the first data set with the word index 204and the hypothesis index 226 may compare the second-word data set 224with the word index 204, wherein the word index 204 may be apre-existing text corpus with a defined context. Therefore, a comparisonbetween the premise index 216 and the hypothesis index 226 may provide acomparison between a context as derived from the premise 212 in regardto a context as derived from the hypothesis. Such a comparison mayfacilitate the generation of the confidence index 228 for thehypothesis. The hypothesis generator 140 may allocate a high confidenceindex 228 to the hypothesis that may have a context closely matching thecontext of the premise 212. The hypothesis generator 140 may allocate alow confidence index 228 to the hypothesis that may have a context notmatching the context of the premise 212. In an example, the hypothesisgenerator 140 may deploy a T-distributed Stochastic Neighbor Embedding(t-SNE) technique wherein similar objects are modeled by nearby pointsand dissimilar objects are modeled by distant points with highprobability.

In accordance with various embodiments of the present disclosure, thehypothesis generator 140 may generate the premise graph 230 and ahypothesis graph 232. The premise graph 230 may be mapping the firstword data set 214 against the second-word data set 224, and thehypothesis graph 232 mapping the second-word data set 224 against thefirst word data set 214. As mentioned above, the premise index 216 maycompare the first data set with the word index 204 and the hypothesisindex 226 may compare the second-word data set 224 with the word index204, wherein the word index 204 may be a pre-existing text corpus with adefined context. The hypothesis generator 140 may map a set of wordsfrom the first word data set 214 with a set of words from thesecond-word data set 224 to derive a relationship between the wordspresent in the premise 212 with regard to words present in thehypothesis in form of the premise graph 230. The premise graph 230 mayfacilitate in establishing a relationship between the premise 212 and ahypothesis from the plurality of hypothesis 222. The hypothesisgenerator 140 may evaluate the relationship between the premise 212 anda hypothesis from the plurality of hypothesis 222 for allocating theconfidence index 228 to the hypothesis. In an example, the hypothesisgenerator 140 may deploy a T-distributed Stochastic Neighbor Embedding(t-SNE) technique for evaluating the premise graph 230. The hypothesisgenerator 140 may map a set of words from the second-word data set 224with a set of words from the first word data set 214 to derive arelationship between the words present in a hypothesis from theplurality of hypothesis 222 with regard to words present in the premise212 in form of the hypothesis graph 232. The hypothesis generator 140may evaluate the relationship between the words present in a hypothesisfrom the plurality of hypothesis 222 with regard to words present in thepremise 212 for allocating the confidence index 228 to the hypothesis.In an example, the hypothesis generator 140 may deploy a T-distributedStochastic Neighbor Embedding (t-SNE) technique for evaluating thehypothesis graph 232. In an example, the hypothesis generator 140 maycompare the premise graph 230 and the hypothesis graph 232 for ahypothesis for allocating the confidence index 228 to the hypothesiswith regard to the premise 212 (explained in detail by way of FIG. 6).In accordance with various embodiments of the present disclosure, thefirst cognitive learning operation 220 may deploy a word embeddingalgorithm such as for example, the Keras ‘pretrained_embedding_layer’,and Glove® word embedding algorithm for determination of the pluralityof hypothesis 222, hypothesis index 226, premise graph 230, andhypothesis graph 232

The modeler 150 implement may a second cognitive learning operation 234to determine an entailment value 236 based on the confidence index 228for each of the plurality of hypothesis 222. In an example, the secondcognitive learning operation 234 may be the NLP model deploying variousword embedding techniques, for example, Keras‘pretrained_embedding_layer’, Word2vec and GloVe vectors to train aneural network to determine if a hypothesis entails a text. In anexample, a Long short-term memory (LSTM) model may be used to determinean entailment value 236 (explained in detail by the way of subsequentFigs.). In an example, a Bi-directional Long Short-Term Memory (BiLSTM)model may be used to determine an entailment value 236 (explained indetail by the way of subsequent Figs.). In an example, a Keras Embeddinglayer may be used to train the system 110. In an example, the KerasEmbedding layer may be initialized using the Glove® word embeddings. Thepremise index 216 and the hypothesis index 226 may be concatenated toinput to the Keras network. The BiLSTM may be deployed to return thepremise index 216 and the hypothesis index 226 values as dotted toobtain an attention layer. The premise index 216 and the hypothesisindex 226 values may be fed as an input to a stack of three 400D® Denselayers. The output may be flattened and fed to a softmax® bottom layerto derive the entailment value 236 (explained in detail by the way ofsubsequent Figs.). The entailment value 236 may be indicating aprobability of a hypothesis from the plurality of hypothesis 222 beingpositively associated with the premise 212.

The modeler 150 implement may the second cognitive learning operation234 to determine a contradiction value 238 from the confidence index 228for each of the plurality of hypothesis 222. The contradiction value 238may be indicating a probability of a hypothesis from the plurality ofhypothesis 222 being negatively associated with the premise 212. Themodeler 150 implement may the second cognitive learning operation 234 todetermine a neutral entailment value 240 from the confidence index 228for each of the plurality of hypothesis 222. The neutral entailmentvalue 240 indicating a probability of a hypothesis from the plurality ofhypothesis 222 being neutrally associated with the premise 212. Themodeler 150 may deploy the same techniques for derivation of thecontradiction value 238 and the neutral entailment value 240 as havingbeen described for the derivation of the entailment value 236.

The modeler 150 implement may the second cognitive learning operation234 to determine an entailed output index 242 by collating theentailment value 236, the contradiction value 238, and the neutralentailment value 240 for each of the plurality of hypothesis 222. Themodeler 150 may determine the entailment value 236, the contradictionvalue 238, and the neutral entailment value 240 for each of theplurality of hypothesis 222 and compare the same. In an example, themodeler 150 may implement the second cognitive learning operation 234for identifying the highest value amongst the entailment value 236, thecontradiction value 238, and the neutral entailment value 240 for eachof the plurality of hypothesis 222. For example, entailment value 236for a hypothesis may be “94.8”, the contradiction value 238 for ahypothesis may be “0”, and the neutral entailment value 240 may be“4.1”. The modeler 150 may present all three of the entailment value236, the contradiction value 238, and the neutral entailment value 240to a user of the system 110. In an example, the modeler 150 may compareall three of the entailment value 236, the contradiction value 238, andthe neutral entailment value 240 and present the highest value to a userof the system 110. The modeler 150 implement may the second cognitivelearning operation 234 to generate an entailment result 244 relevant forresolving the query. The entailment result 244 may be comprising theplurality of hypothesis 222 along with the corresponding entailed outputindex 242. In an example, the entailment result 244 may further includean entailment output corresponding to the highest value from theentailed output index 242 associated with each of the plurality ofhypothesis 222.

In accordance with various embodiments of the present disclosure, theentailment data organizer 130 may further establish an entailment datalibrary by associating entailment data with the premise 212, theplurality of hypothesis 222 and the confidence index 228 for each of theplurality of hypothesis 222. The system 110 may deploy inputs from theentailment data library for improving the efficiency of the firstcognitive learning operation 220 and the second cognitive learningoperation 234.

In operation, the system 110 may generate a set of textual entailmentsfrom a given text document. For example, the system 110 may generateinsights about a particular drug from interpreting associated medicalliterature, patient records and the like for pharmacovigilance purposes.The system 110 may generate the set of textual entailments from a giventext document through deployment of the processor 120, which may becoupled to the entailment data organizer 130, hypothesis generator 140and the modeler 150. The entailment data organizer 130 may receive thedata entailment requirement 202 and associated entailment data from auser. The entailment data may be a text document or a set of textdocuments. The entailment data organizer 130 may implement atokenization operation (explained in detail by way of subsequent Figs.)to break the entailment data into a set of meaningful fragments. The setof meaningful fragments may comprise a set of premise 212 for generationof entailment insights. The system 110 may break the premise 212 intothe first word data set 214 and map the words therein with the wordindex 204, which may be a text corpus for developing the premise index216. In an example, the system 110 may deploy a word embedding algorithmas part of the artificial intelligence component 218 for naturallanguage processing aiming at mapping semantic meaning of the premise212. This may be done by associating a numeric vector to every word inthe first word data set 214 from the plurality of words 208 from thepremise 212, such that the distance between any two vectors wouldcapture part of the semantic relationship between the two associatedwords. The geometric space formed by these vectors may be referred as anembedding space. The knowledge database 206 may include all the wordsmentioned therein labeled with semantic relationships. For example, asmentioned above, the first word data set 214 for the premise 212 mayinclude words such as “treatment”, “for”, “angina”, “pectoris”, “with”,“dipyridamole”, “220”, “mg”, “daily”, “and”, “verapamil”, “120”, “mg”,“daily”, “had”, “been”, “started”, “three”, “months”, “and”, “two”,“weeks”, “previously”, “respectively”. The artificial intelligencecomponent 218 may deploy word embeddings to decipher that the words“angina”, and “pectoris” may be syntactically related and hence theywould be placed in vicinity of each other and interpreted accordingly.Additionally, the artificial intelligence component 218 may deploy wordembeddings to decipher that the words “daily”, “and”, “verapamil” may berelated in terms that one may be a drug name and another may be a drugdosage timing and therefore they may be placed and interpretedaccordingly.

The system 110 may deploy the hypothesis generator 140 to generate aplurality of hypothesis 222 for the premise 212 based in the contextinterpreted from the premise index 216. The hypothesis generator 140 mayobtain the context of the premise 212 from the entailment data organizer130. The hypothesis generator 140 may deploy the recurrent NeuralNetwork-based attention model and a word embedding algorithm forgenerating the plurality of hypothesis 222, wherein a sentence may bepredicted and formed based on a previous sentence. For example, thepremise 212 may be “treatment for angina pectoris with dipyridamole 220mg daily and verapamil 120 mg daily had been started three months andtwo weeks previously, respectively”. The hypothesis generator 140 maygenerate a first hypothesis as “Dipyridamole is an ongoing medication”,a second hypothesis as “Dipyridamole is not an ongoing medication”, athird hypothesis as “Verapamil is a concomitant drug”, and a fourthhypothesis as “Verapamil is not a concomitant drug”. The hypothesisgenerator may identify words from deploying the recurrent NeuralNetwork-based attention model on the premise index 216 and compare itwill the word index 204 for generation of the second-word data set 224and form the plurality of hypothesis 222 therefrom.

As mentioned above, each of the plurality of hypothesis 222 may beindicating an inference associated with the premise 212. Each of theplurality of hypothesis 222 may include the second-word data set 224.The hypothesis generator 140 implements the word embedding algorithm aspart of the first cognitive learning operation 220 for mapping thesecond-word data set 224 from hypothesis with the word index 204 fordetermining the hypothesis index 226. The hypothesis index 226 andpremise index 216 may be compared for determining the confidence index228 for the hypothesis. The hypothesis generator 140 may compare thehypothesis index with the premise index 216 by deploying the wordembedding algorithm and mapping various words present therein (explainedfurther by way of FIG. 6). The modeler 150 may determine the entailmentvalue 236 based on the confidence index 228. The modeler 150 mayevaluate the confidence index 228 and determine a probability of ahypothesis from the plurality of hypothesis 222 for the premise 212 maybe an entailment. The probability may be referred of the entailmentvalue 236. For example, the premise 212 may be “treatment for anginapectoris with dipyridamole 220 mg daily and verapamil 120 mg daily hadbeen started three months and two weeks previously, respectively”. Thehypothesis generator 140 may generate a first hypothesis as firsthypothesis as “Dipyridamole is an ongoing medication”, and the secondhypothesis as “Dipyridamole is not an ongoing medication”. Theconfidence index 228 may be determined by comparing the words andpresent in the first hypothesis with the words present in the premise212 along with the context of words present in the premise 212 with thecontext of the words present in the first hypothesis. The word“Dipyridamole” may be present in the premise 212 and the firsthypothesis. The phrase “had been started three months and two weekspreviously” may be mapped with the word index 204 while determining thepremise index 216 and the word “ongoing” may be established by thesystem 110 to convey the same meaning. Hence, the word “Dipyridamole”may be positively related to the word “ongoing”, while generating thefirst hypothesis. Additionally, the word “previously” may indicate anegative association with the word “Dipyridamole” and hence it may benegatively related to the word “ongoing” while generating the secondhypothesis.

The confidence index 228 may be determined by the system 110 for thefirst hypothesis to include a probability of an entailment of the firsthypothesis on the premise 212, a probability of a contradiction of thefirst hypothesis on the premise 212, and a probability of a neutralentailment of the first hypothesis on the premise 212. The probabilityof the entailment of the first hypothesis on the premise 212 may bereferred to as the entailment value 236 for the first hypothesis. Theprobability of the contradiction of the first hypothesis on the premise212 may be referred to as the contradiction value 238 for the firsthypothesis. The probability of the neutral entailment of the firsthypothesis on the premise 212 may be referred to as the neutralentailment value 240 for the first hypothesis. The modeler 150 maycollate the entailment value 236, the contradiction value 238, and theneutral entailment value 240 for the first hypothesis for determinationof the entailed output index 242. The modeler 150 may compare theentailment value 236, the contradiction value 238, and the neutralentailment value 240 from the entailed output index 242 for the firsthypothesis and determine which, of these values may be highest. Themodeler 150 may generate the entailment result 244 based on the valuethat is highest from the entailment value 236, the contradiction value238, and the neutral entailment value 240 for the first hypothesis forthe premise 212. The modeler 150 may repeat the aforementionedcomparison for each of the confidence indices for each of the hypothesisand generate the entailment result 244. For example, the modeler 150 maypresent the first hypothesis to be an “entailment” based on thecomparison of the hypothesis index 226 for the first hypothesis with thepremise index 216 and subsequent confidence index 228. Additionally, themodeler 150 may present the second hypothesis to be a “contradiction”based on the comparison of the hypothesis index 226 for the secondhypothesis with the premise index 216 and subsequent confidence index228.

In accordance with various embodiments of the present disclosure, themodeler 150 may collate each of the plurality of hypothesis 222 wherein,the entailment value 236 may be the highest and present the same to theuser as part of the entailment result 244.

Accordingly, the system 110 and various embodiments thereof may providean effective and efficient entailment analysis for a text document. Thesystem 110 may be more effective due to the use of a Keras pre-trainedembedding layer along with a Glove® word embedding model implementedtherein part from various other artificial intelligence techniques. Thesystem 110 may deploy various attention layer-based algorithms fordetermination of the premise index 216, the hypothesis index 226, thepremise graph 230, and the hypothesis graph 232.

FIG. 3 illustrates a network architectural diagram for a system 300 forgenerating textual entailment using a textual entailment system,according to an example embodiment of the present disclosure. Any of thecomponents of the system 110 as described by the way of FIG. 1 and FIG.2 may be deployed by the system 300 for generating textual entailment.

In accordance with various embodiments of the present disclosure, thesystem 300 may receive the data entailment requirement 202. The system300 may deploy a recurrent neural network (RNN) for generating textualentailment. The system 300 may deploy a BiDirectional LSTM model usingattention model for generating textual entailment. The BiDirectionalLSTM model may process the context from the premise index 216 and thehypothesis index 226 more efficiently due to its bidirectional nature.The BiDirectional LSTM model may interpret the premise 212 in a forwarddirection and in a backward direction at the same time, therebyincreasing the probability of a correct entailment generation. Forexample, if the premise 212 may be “The reporter considered thepneumonia to be related to Kemstro, sepsis to be related to Kemstro andurinary tract infection to be related to Kemstro.” The BiDirectionalLSTM model may map the premise 212 from beginning to end of the premise212 sentence and from end to beginning of the premise 212 sentencesimultaneously for generating a context effectively. The premise index216 generated by deployment of the BiDirectional LSTM model may be moreefficient. The system 300 may include a word index 204 302 and a wordembedding component 304. In an example, the word embedding algorithm maybe GloVe word embedding model. The word index 204 302 may be similar tothe word index 204. The word embedding component 304 may implement anyof the word embedding algorithms for processing the data entailmentrequirement 202. The system 300 may include a series of RNN componentblocks. The RNN component blocks may include an RNN block 306, an RNNblock 308, an RNN block 310, an RNN block 312, an RNN block 316, and anRNN block 318. In an example, the RNN block 306, the RNN block 308, andthe RNN block 310 may map the premise 212 from an end and move towardsthe beginning of the premise 212 sentence. The RNN block 312, the RNNblock 314, and the RNN block 316 may map the premise 212 from thebeginning of the premise 212 sentence and move towards an end of thepremise 212 sentence. The system 300 may include a series of summationblocks. The series of summation blocks may include a sum 318, a sum 320,and a sum 322. In an example, the sum 318 may be a summation of theinterpretation from the RNN block 306 and the RNN block 312. The sum 320may be a summation of the interpretation from the RNN block 308 and theRNN block 314. The sum 322 may be a summation of the interpretation fromthe RNN block 310 and the RNN block 316. The series of RNN componentblocks may identify the premise 212 and may generate the plurality ofhypothesis 222.

The system 300 may include an attention layer 324. The sum 318, the sum320, and the sum 322 may provide an input to the attention layer 324.The attention layer 324 may take two sentences, turns them into a matrixwhere the words of one sentence form the columns, and the words ofanother sentence form the rows, and then it may make matches,identifying relevant context. The attention layer 324 may receive inputsfrom the sum 318, the sum 320, and the sum 322 to determine the premiseindex 216, the hypothesis index 226, the premise graph 230, and thehypothesis graph 232. In an example, the attention layer 324 may includethe Keras attention layer. The system may further include a series of adense layer 326. The dense layer 326 may a fully connected neuralnetwork layer. For the sake of brevity and technical clarity, detailsinformation on the dense layer 326 may not be presented herein butshould be clear to a person skilled in the art. In an example, the denselayer 326 may flatten the input received from the attention layer 324.The attention layer 324 may generate an output in form of a matrix withrows and columns mapped with each other. The dense layer 326 may flattenthe matrix into a single column output data. The dense layer 326 maypass on the flattened single column output data to a softmax bottomlayer for generating textual entailment.

FIG. 4 illustrates a flow diagram for a configuration of a Kerasembedding layer 400 for generating textual entailment using a textualentailment system, according to an example embodiment of the presentdisclosure. Any of the components of the system 110 as described by theway of FIG. 1, FIG. 2 and FIG. 3 may be deployed by the Keras embeddinglayer 400 for generating textual entailment.

As mentioned above, the Word embeddings may be a set of natural languageprocessing techniques aiming at mapping semantic meaning into a vectorspace. This may be achieved by associating a numeric vector to everyword in word index 204, the premise 212, and each of the plurality ofthe hypothesis such that the distance between any two vectors wouldcapture part of the semantic relationship between the two associatedwords. The geometric space formed by these vectors is called anembedding space. For example, “disorder” and “song” may be words thatmay be semantically quite different, so a reasonable embedding space mayrepresent them as vectors that may be far apart. But “disorder” and“medication” are related words, so they may be embedded close to eachother.

The Keras embedding layer 400 may include an initialization component402. The initialization component 402 may initialize the knowledgedatabase 206 upon receiving the data entailment requirement 202 andassociated entailment data. The initialization component 402 may provideinput to a decision component 404. The decision component may identify aword from the entailment data and compare it with the knowledge database206. In an example, the decision component 404 may identify a word fromthe first word data set 214 and compare it with the second-word data set224. The decision component 404 may return the input provided by theinitialization component 402 to an embedding layer 408, when the wordfrom the entailment data may not be matched with a word from theknowledge database 206. The decision component 404 may form a matrixcomponent 406 when the word from the entailment data may be matched witha word from the knowledge database 206. In an example, the matrixcomponent 406 may be the premise index 216, the hypothesis index 226.The matrix component 406 may pass on a matrix output to the decisioncomponent 404 for validation of the matrix created by the matrixcomponent 406 may lead to the embedding layer 408, wherein respectiveweights may be provided to various words based on the mappings.

FIG. 5 illustrates a flow diagram for a process 500 for initializing anarray of indices corresponding to words in the word index 204 using atextual entailment system, according to an example embodiment of thepresent disclosure. Any of the components of the system 110 as describedby the way of FIG. 1, FIG. 2, FIG. 3 and FIG. 4 may be deployed forinitializing the array of indices corresponding to the sentence words inthe word index 204. The process 500 may use the GloVe dictionary. TheGlove® may refer to a pre-computed database of word embeddings developedby Stanford researchers in 2014. It stands for “Global Vectors for WordRepresentation”, and it may be an embedding technique based onfactorizing a matrix of word co-occurrence statistics. The process 500deploy the Glove® embeddings in a Keras model. The process 500 mayinclude an initialization 502. The initialization 502 may execute a“len” function and an “x_indices” function. The “len” function may referto an inbuilt function in Python™ programming language that may return alength of the string. The “len” function may calculate number of wordsin the entailment data. In an example, the user may define an upperlimit for the “len” function, for example, to 50 words. In such anexample, the system 110 may identify the premise 212 such that eachpremise 212 may have less than 50 words. The “x_indices” function mayrefer to an array of indices corresponding to words in the sentencesfrom a point X. The “x_indices” function may facilitate premise 212determination from the entailment data. In an example, “x_indices”function may facilitate hypothesis determination from the premise index216. The initialization 502 may be followed by a comparison 504, whereinan “i++<len” function may be executed. The “i” from the ““i++<len”function” may refer to a number of characters in the premise 212identified by the initialization 502. In an example, the “i” from the““i++<len” function” may refer to a number of characters in a hypothesisfrom the plurality of hypothesis 222 identified by the initialization502. The comparison 504 may compare a number of words in the premise 212determined by the initialization 502 with the number of words defined byupper limit for the “len” function. If the number of words in thepremise 212 may be less than the upper limit for the “len” function, theprocess 500 may execute a word index identification 506, wherein theprocess may execute a “w in sentence_words” function. The ““w insentence_words” function may identify the word index 204 for the premise212 determined by the “x_indices” function. If the number of words inthe premise 212 may not be less than the upper limit for the “len”function, the process 500 may execute a return function 512, wherein theprocess may execute a “return x_indices” function. The word indexidentification 506 may be followed by a matrix creation 508, wherein a“x_indices[l, j]=word_to_index[w]” function may be executed. The“x_indices[l, j]=word_to_index[w]” function may facilitate creation of amatrix for determination of the premise index 216 wherein, “i” may referto the first words data set and “j” may refer to the word index 204. Inan example, “x_indices[l, j]=word_to_index[w]” function may facilitatecreation of a matrix for determination of the hypothesis index 226wherein “i” may refer to the second words data set and “j” may refer tothe word index 204. In an example, the “x_indices[l,j]=word_to_index[w]” function may facilitate creation of a matrix fordetermination of the premise graph 230, wherein “i” may refer to thatfirst words data set and “j” may refer to the second-word data set 224.In an example, the “x_indices[l, j]=word_to_index[w]” function mayfacilitate creation of a matrix for determination of the hypothesisgraph 232, wherein “i” may refer to that second words data set and “j”may refer to the first word data set 214.

The matrix creation 508 may be followed by a comparison of 510. Thecomparison 510 may evaluate a number of characters in the “j” componentof the matrix created by the matrix creation 508. If the number ofcharacters in the “j” component may be less than a maximum sequencelength defined by the user, the process 500 may execute the word indexidentification 506. If the number of characters in the “j” component maynot be less than a maximum sequence length defined by the user, theprocess 500 may execute the comparison 504. For the sake of brevity,further details about various functions may be explained in detailhereinafter, however the same should be clear to a person skilled in theart.

FIG. 6A illustrates a pictorial representation 600A of the premise 212mapping with a hypothesis using a textual entailment system, accordingto an example embodiment of the present disclosure. Any of thecomponents of the system 110 as described by the way of FIG. 1, FIG. 2,FIG. 3, FIG. 4 and FIG. 5 may be deployed by the pictorialrepresentation 600. In an example, the pictorial representation 600A maydepict a set of graphs corresponding to the attention layer in theneural network model described by way of FIG. 2 and FIG. 3. Inaccordance with various embodiments of the present disclosure, thepictorial representation 600A may include a first quadrant 620, a secondquadrant 602, a third quadrant 604, and a fourth quadrant 618. In anexample, each of the first quadrant 620, the second quadrant 602, thethird quadrant 604, and the fourth quadrant 618 in the pictorialrepresentation 600A may contain the premise 212-hypothesis attentionsfor the concatenated premise 212-hypothesis sentence strings. In anexample, each string may have a length of 50 characters with aconcatenated string of length 100 characters. In an example, the secondquadrant 602 may represent the premise graph 230 and the third quadrant604 may represent the hypothesis graph 232.

FIG. 6B illustrates a pictorial representation 600B of the premise 212mapping with a hypothesis from the plurality of hypothesis 222 using atextual entailment system, according to an example embodiment of thepresent disclosure. The pictorial representation 600B includes a graph606 and a graph 608. For example, the system 110 may identify thepremise 212 as “On 20 May 2016, the patient started therapy with Provas(valsartan), tablet, at 80 mg once daily via oral route forhypertension. On 20 Mar. 2018, the patient was hospitalized forMyocardial infarction and congestive cardiac failure. The patient wasdied on the same day due to Myocardial infarction”. Additionally, thesystem 110 may identify a hypothesis from the plurality of hypothesis222 to be “Provas (valsartan) has caused Myocardial infarction andcongestive cardiac failure”. The graph 606 may represent the premisegraph 230 wherein, words from the exemplary premise 212 mentioned abovemay be mapped with the words from the exemplary hypothesis mentionedabove. The graph 608 may represent the hypothesis graph 232, wherein,words from the exemplary hypothesis mentioned above may be mapped withthe words from the exemplary premise 212 mentioned above.

FIG. 6C illustrates a pictorial representation 600C of the premise 212mapping with a hypothesis from the plurality of hypothesis 222 using atextual entailment system, according to an example embodiment of thepresent disclosure. The pictorial representation 600B includes a graph610 and a graph 612. For example, the system 110 may identify thepremise 212 as “This church choir sings to the masses as they singjoyous songs from the book at a church”. Additionally, the system 110may identify a hypothesis from the plurality of hypothesis 222 to be“The church is filled with song”. The graph 610 may represent thepremise graph 230 wherein, words from the exemplary premise 212mentioned above may be mapped with the words from the exemplaryhypothesis mentioned above. The graph 612 may represent the hypothesisgraph 232, wherein, words from the exemplary hypothesis mentioned abovemay be mapped with the words from the exemplary premise 212 mentionedabove

FIG. 6D illustrates a pictorial representation 600D of the premise 212mapping with a hypothesis from the plurality of hypothesis 222 using atextual entailment system, according to an example embodiment of thepresent disclosure. The pictorial representation 600B includes a graph614 and a graph 616. For example, the system 110 may identify thepremise 212 as “A woman with a green headscarf, blue shirt, and a verybig grin”. Additionally, the system 110 may identify a hypothesis fromthe plurality of hypothesis 222 to be “The woman is very happy”. Thegraph 614 may represent the premise graph 230 wherein, words from theexemplary premise 212 mentioned above may be mapped with the words fromthe exemplary hypothesis mentioned above. The graph 616 may representthe hypothesis graph 232, wherein, words from the exemplary hypothesismentioned above may be mapped with the words from the exemplary premise212 mentioned above

FIG. 7A illustrates a flow diagram for a tokenization operation 700,according to an example embodiment of the present disclosure. Any of thecomponents of the system 110 as described by the way of FIG. 1, FIG. 2,FIG. 3, FIG. 4, FIG. 5. And FIG. 6 may be deployed by the system 110 forthe tokenization operation 700. The tokenization may be a task ofchopping the entailment data various into pieces, called tokens andremove certain characters, such as punctuation. In an example, thetokenizer may transform text into a set of vectors, which may be usedfor word embedding. The tokenization operation 700 may include a“tokenizer.fit_on_texts( )” function and Keras “model.fit( ) training”function. The trained models may be saved and pickled for future use.These serialized models may be loaded onto a web service to determineentailment information related with the entailment data. In an example,the tokenization operation 700 may determine a token, which may be usedto tokenize out of the word index 204, the first word data set 214 andthe second-word data set 224 in a tokenizer class corresponding to thepremise index 216 and hypothesis index 226.

The tokenization operation 700 may include an initialization 702. Theinitialization 702 may initiate an RNN model and load the entailmentdata onto the system 110. The initialization 702 may be followed by acomparison 704. The comparison 704 may evaluate the entailment data byexecuting an “isTrain” function. In an example, wherein the entailmentdata may not be trained, the tokenization operation 700 may execute afunction 716. The function 716 may be “tokenizer

pickle.load(tokenizer_file)” function. The function 716 may be followedby a function 718. In an example, the function 718 may be “model

load_model(model_file)” function. In an example, wherein the entailmentdata may be trained, the tokenization operation 700 may execute afunction 706. The function 706 may be “tokenizer

Tokenizer( )fit_on_texts(train_premise 212+train_hyp)” function. Thefunction 716 may execute a picklization 708 for a text created as anoutput for the “tokenizer→TokenizerQ.fit_on_texts(train_premise212+train_hyp)” function. The picklization 708 may execute a“pickle.dump(tokenizer, tokenizer file)” function. The function 706 maybe followed by a function 710, which may create tokenization models. Inan example, the function 710 may be “model→lstm-entailment (shape,units)” function. The function 710 may be followed by a function 712. Inan example, the function 712 may be “model.fit (x_train, z_train,validation_data→(x_val, z_val))” function. The function 712 be followedby a function 714, wherein the tokenization operation 700 may execute a“model.save(model_file)” for saving the trained models created by thefunction 712. The tokenization operation 700 may execute a modelreturning function 720 after executing the function 712 and the function718. The model returning function 720 may return tokenized strings forthe premise 212 and the plurality of hypothesis 222. For sake ofbrevity, further details about various functions are not mentionedherein, however, the same should be clear to a person skilled in theart.

FIG. 7B illustrates a flow diagram for a process 800 for prediction ofan entailment using a textual entailment system, according to an exampleembodiment of the present disclosure. Any of the components of thesystem 110 as described by the way of FIG. 1, FIG. 2, FIG. 3, FIG. 4,FIG. 5, FIG. 6 and FIG. 7 may be deployed by the system 110 for theprocess 800. The process 800 may include an initialization 802, whereinthe entailment data may be loaded onto the system 110. Theinitialization 802 may be followed by a model loading 804, wherein thetokenization models created by the tokenization operation 700. Theprocess 800 may further include a sequencer 806. The sequencer 806 maygenerate sequences for the premise 212 and the plurality of hypothesis222 based on the tokenization models. In an example, the sequencer 806may execute a “sequences_premise 212→tokenize.texts_to_sequences(premise212)” function, a “sequences_premise 212

pad_sequences(sequences_premise 212)” function, a sequences_hyp

tokenizer.texts_to_sequences(hypothesis)” function, and a “sequences_hyp

pad_sequences(sequences_hyp)” function. In an example, the sequencer 806may determine the premise index 216 and the hypothesis index 226. Theprocess 800 may further include a concatenation 808. The concatenation808 may execute a “input

concatenate (sequences_premise 212, sequences_hyp)” function. Theconcatenation 808 may determine the premise graph 230 and the hypothesisgraph 232. The concatenation 808 may be followed by a function 810. Thefunction 810 may execute a function “input

concatenate(sequences_premise 212, sequences_hyp)” The function 810 maydetermine the confidence index 228 for the plurality of hypothesis 222from the premise index 216, the hypothesis index 226, the premise graph230, the hypothesis graph 232 determined by the concatenation 808 andthe sequencer 806. The process 800 may further include a function 812.The function 812 may be followed by the function 810. The function 812may deploy a SOAP® (Simple Object Access Protocol) or a REST®(Representational State Transfer) web service or may be used todetermine entailment information. The function 812 may deploy a JSON®(JavaScript Object Notation) to determine entailment information. Theprocess 800 may include providing an entailment response as aprobability confidence score corresponding to the entailed output. In anexample, the function 812 may be followed by a function 814. Thefunction 814 may determine the entailment value 236, the contradictionvalue 238, the neutral entailment value 240, and the entailed outputindex 242.

For example, for the premise 212 “Other causes of rhabdomyolysis wereexcluded and expert opinion agreed that the most likely cause was theinfluenza vaccination with the concurrent use of simvastatin.”, acorresponding hypothesis may be “Simvastatin has caused rhabdomyolysis.”The function 814 may provide the entailment response as a probabilityconfidence score to be “neutral”: 4.2, “contradiction”: 0, and“entailment”: 94.8. In another example, for the premise 212 “Two youngchildren in blue jerseys, one with the number 9 and one with the number2 are standing on wooden steps in a bathroom and washing their hands ina sink.”, a corresponding hypothesis may be “Two kids in jackets walk toschool.” The function 814 may provide the entailment response as aprobability confidence score to be “neutral”: 3.5, “contradiction”:96.3, and “entailment”: 0. The function 814 may provide the probabilityconfidence score corresponding to the entailed output to the function812. The function 812 may execute a function 816. The function 816 mayprovide the entailment result 244 to the user.

FIG. 8 illustrates a hardware platform 900 for implementation of thesystem 110, according to an example embodiment of the presentdisclosure. Particularly, computing machines such as but not limited tointernal/external server clusters, quantum computers, desktops, laptops,smartphones, tablets and wearables which may be used to execute thesystem 110 or may have the structure of the hardware platform 900. Thehardware platform 900 may include additional components not shown andthat some of the components described may be removed and/or modified. Inanother example, a computer system with multiple GPUs can sit onexternal-cloud platforms including Amazon Web Services, or internalcorporate cloud computing clusters, or organizational computingresources, etc.

Over FIG. 8, the hardware platform 900 may be a computer system 900 thatmay be used with the examples described herein. The computer system 900may represent a computational platform that includes components that maybe in a server or another computer system. The computer system 900 mayexecute, by a processor (e.g., a single or multiple processors) or otherhardware processing circuit, the methods, functions and other processesdescribed herein. These methods, functions and other processes may beembodied as machine-readable instructions stored on a computer-readablemedium, which may be non-transitory, such as hardware storage devices(e.g., RAM (random access memory), ROM (read-only memory), EPROM(erasable, programmable ROM), EEPROM (electrically erasable,programmable ROM), hard drives, and flash memory). The computer system900 may include a processor 905 that executes software instructions orcode stored on a non-transitory computer-readable storage medium 910 toperform methods of the present disclosure. The software code includes,for example, instructions to gather data and documents and analyzedocuments. In an example, the entailment data organizer 130, thehypothesis generator 140 and the modeler 150 may be software codes orcomponents performing these steps.

The instructions on the computer-readable storage medium 910 are readand stored the instructions in storage 915 or in random access memory(RAM) 920. The storage 915 provides a large space for keeping staticdata where at least some instructions could be stored for laterexecution. The stored instructions may be further compiled to generateother representations of the instructions and dynamically stored in theRAM 920. The processor 905 reads instructions from the RAM 920 andperforms actions as instructed.

The computer system 900 further includes an output device 925 to provideat least some of the results of the execution as output including, butnot limited to, visual information to users, such as external agents.The output device can include a display on computing devices and virtualreality glasses. For example, the display can be a mobile phone screenor a laptop screen. GUIs and/or text are presented as an output on thedisplay screen. The computer system 900 further includes input device930 to provide a user or another device with mechanisms for enteringdata and/or otherwise interact with the computer system 900. The inputdevice may include, for example, a keyboard, a keypad, a mouse, or atouchscreen. In an example, the output of the hypothesis generator 140and the modeler 150 may be displayed on the output device 925. Each ofthese output devices 925 and input devices 930 could be joined by one ormore additional peripherals. In an example, the output device 925 may beused to display the results of the data entailment requirement 202.

A network communicator 935 may be provided to connect the computersystem 900 to a network and in turn to other devices connected to thenetwork including other clients, servers, data stores, and interfaces,for instance. A network communicator 935 may include, for example, anetwork adapter such as a LAN adapter or a wireless adapter. Thecomputer system 900 includes a data source interface 940 to access datasource 945. A data source is an information resource. As an example, adatabase of exceptions and rules may be a data source. Moreover,knowledge repositories and curated data may be other examples of datasources.

FIGS. 9A and 9B illustrate a method 1000 for the textual entailmentsystem 110 according to an example embodiment of the present disclosure.

It should be understood that method steps are shown here for referenceonly and other combinations of the steps may be possible. Further, themethod 1000 may contain some steps in addition to the steps shown inFIGS. 9A and 9B For the sake of brevity, construction and operationalfeatures of the system 100 which are explained in detail in thedescription of FIG. 1, FIG. 2, FIG. 3, FIG. 4, FIG. 5, FIG. 6A, FIG. 6B,FIG. 6C, FIG. 6D, FIGS. 7A and 7B, and FIG. 8 are not explained indetail in the description of FIGS. 9A and 9B. The method 1000 may beperformed by a component of the system 110, such as the processor 120,the entailment data organizer 130, the hypothesis generator 140 and themodeler 150.

At block 1002, a query may be obtained from a user. The query may beindicating a data entailment requirement 202 comprising entailment dataand associated with the entailment operations.

At block 1004, the artificial intelligence component 218 may beimplemented by the system 110.

At block 1006, the artificial intelligence component 218 may beimplemented to identify a word index 204 from a knowledge database 206.The word index 204 may be including a plurality of words 208 beingassociated with the entailment requirement. In an example, the knowledgedatabase 206 is a natural language data directory.

At block 1008, the artificial intelligence component 218 may beimplemented to identify the premise 212 from the entailment data. Thepremise 212 may be comprising the first word data set 214 to beassociated with the data entailment requirement 202.

At block 1010, the artificial intelligence component 218 may beimplemented to determine a premise index 216 by mapping the first worddata set 214 with the word index 204.

At block 1012, the first cognitive learning operation 220 may beimplemented to determine a plurality of hypothesis 222 corresponding tothe premise 212. In an example, each of the plurality of hypothesis 222may be comprising a second-word data set 224 and indicating an inferenceassociated with the premise 212. The second-word data set 224 may beassociated with the word index 204.

At block 1014, a hypothesis index 226 may be determined by mapping thesecond-word data set 224 with the word index 204.

At block 1016, a confidence index 228 may be generated for each of theplurality of hypothesis 222 based on a comparison of the hypothesisindex 226 with the premise index 216.

At block 1018, the entailment value 236 may be determined based on theconfidence index 228 for each of the plurality of hypothesis 222. Theentailment value 236 may be indicating a probability of a hypothesisfrom the plurality of hypothesis 222 being positively associated withthe premise 212.

At block 1020, the contradiction value 238 may be determined from theconfidence index 228 for each of the plurality of hypothesis 222. Thecontradiction value 238 indicating a probability of a hypothesis fromthe plurality of hypothesis 222 being negatively associated with thepremise 212.

At block 1022, the neutral entailment value 240 may be determined fromthe confidence index 228 for each of the plurality of hypothesis 222.The neutral entailment value 240 indicating a probability of ahypothesis from the plurality of hypothesis 222 being neutrallyassociated with the premise 212.

At block 1024, the entailed output index 242 may be determined bycollating the entailment value 236, the contradiction value 238, and theneutral entailment value 240 for each of the plurality of hypothesis222.

At block 1026, the entailment result 244 relevant may be generated forresolving the query. The entailment result 244 comprising the pluralityof hypothesis 222 along with the corresponding entailed output index242. In an example, the entailment result 244 further including anentailment output corresponding to the highest value from the entailedoutput index 242 associated with each of the plurality of hypothesis222.

In an example, the method 1000 may further include generating thepremise graph 230 and the hypothesis graph 232. The premise graph 230may be mapping the first word data set 214 against the second-word dataset 224, and the hypothesis graph 232 may be mapping the second-worddata set 224 against the first word data set 214. In an example, theconfidence index 228 may be generated by comparing the premise graph 230and the hypothesis graph 232. The method 1000 may further includeimplementing the second cognitive learning operation 234 for identifyingthe highest value amongst the entailment value 236, the contradictionvalue 238, and the neutral entailment value 240 for each of theplurality of hypothesis 222.

In an example, the method 1000 may comprise creating the entailment datalibrary by associating entailment data with the premise 212, theplurality of hypothesis 222 and the confidence index 228 for each of theplurality of hypothesis 222.

In an example, the method 1000 may be practiced using a non-transitorycomputer-readable medium. In an example, the method 1000 may be acomputer-implemented method.

The present disclosure provides for a textual entailment system whichmay generate textual insights while incurring minimal costs.Furthermore, the present disclosure may categorically analyze variousparameters that may have an impact on the generation of a hypothesis fortextual entailment and analyze a document presented for entailmentaccordingly.

One of ordinary skill in the art will appreciate that techniquesconsistent with the present disclosure are applicable in other contextsas well without departing from the scope of the disclosure.

What has been described and illustrated herein are examples of thepresent disclosure. The terms, descriptions, and figures used herein areset forth by way of illustration only and are not meant as limitations.Many variations are possible within the spirit and scope of the subjectmatter, which is intended to be defined by the following claims andtheir equivalents in which all terms are meant in their broadestreasonable sense unless otherwise indicated.

I/we claim:
 1. A system comprising: a processor; an entailment dataorganizer coupled to the processor, the entailment data organizer to:obtain a query from a user, the query indicating a data entailmentrequirement comprising entailment data and associated with entailmentoperations; and implement an artificial intelligence component to:identify a word index from a knowledge database, the word indexincluding a plurality of words being associated with the data entailmentrequirement; identify a premise from the entailment data, the premisecomprising a first word data set associated with the data entailmentrequirement; and determine a premise index by mapping the first worddata set with the word index; a hypothesis generator coupled to theprocessor, the hypothesis generator to: implement a first cognitivelearning operation to determine a plurality of hypothesis correspondingto the premise, each of the plurality of hypothesis comprising a secondword data set and indicating an inference associated with the premise,the second word data set being associated with the word index; determinea hypothesis index by mapping the second word data set with the wordindex; and generate a confidence index for each of the plurality ofhypothesis based on a comparison of the hypothesis index with thepremise index; and a modeler coupled to the processor, the modeler toimplement a second cognitive learning operation to: determine anentailment value based on the confidence index for each of the pluralityof hypothesis, the entailment value indicating a probability of ahypothesis from the plurality of hypothesis being positively associatedwith the premise; determine a contradiction value from the confidenceindex for each of the plurality of hypothesis, the contradiction valueindicating a probability of a hypothesis from the plurality ofhypothesis being negatively associated with the premise; and determine aneutral entailment value from the confidence index for each of theplurality of hypothesis, the neutral entailment value indicating aprobability of a hypothesis from the plurality of hypothesis beingneutrally associated with the premise; determine an entailed outputindex by collating the entailment value, the contradiction value, andthe neutral entailment value for each of the plurality of hypothesis;and generate an entailment result relevant for resolving the query, theentailment result comprising the plurality of hypothesis along with thecorresponding entailed output index.
 2. The system as claimed in claim1, wherein the knowledge database is a natural language data directory.3. The system as claimed in claim 1, wherein the hypothesis generator isto generate a premise graph and a hypothesis graph, the premise graphmapping the first word data set against the second word data set, andthe hypothesis graph mapping the second word data set against the firstword data set.
 4. The system as claimed in claim 3, wherein thehypothesis generator is to generate the confidence index by comparingthe premise graph and the hypothesis graph.
 5. The system as claimed inclaim 1, wherein the modeler implements the second cognitive learningoperation for identifying a highest value amongst the entailment value,the contradiction value, and the neutral entailment value for each ofthe plurality of hypothesis.
 6. The system as claimed in claim 5,wherein the entailment result further includes an entailment outputcorresponding to the highest value from the entailed output indexassociated with each of the plurality of hypothesis.
 7. The system asclaimed in claim 1, wherein the entailment data organizer is to furtherestablish an entailment data library by associating entailment data withthe premise, the plurality of hypothesis and the confidence index foreach of the plurality of hypothesis.
 8. A method comprising: obtaining,by a processor, a query from a user, the query indicating a dataentailment requirement comprising entailment data and associated withentailment operations; implementing, by the processor, an artificialintelligence component to: identify a word index from a knowledgedatabase, the word index including a plurality of words being associatedwith the data entailment requirement; identify a premise from theentailment data, the premise comprising a first word data set associatedwith the data entailment requirement; and determine a premise index bymapping the first word data set with the word index; implementing, bythe processor, a first cognitive learning operation to determine aplurality of hypothesis corresponding to the premise, each of theplurality of hypothesis comprising a second word data set and indicatingan inference associated with the premise, the second word data set beingassociated with the word index; determining, by the processor, ahypothesis index by mapping the second word data set with the wordindex; generating, by the processor, a confidence index for each of theplurality of hypothesis based on a comparison of the hypothesis indexwith the premise index; determining, by the processor, an entailmentvalue based on the confidence index for each of the plurality ofhypothesis, the entailment value indicating a probability of ahypothesis from the plurality of hypothesis being positively associatedwith the premise; determining, by the processor, a contradiction valuefrom the confidence index for each of the plurality of hypothesis, thecontradiction value indicating a probability of a hypothesis from theplurality of hypothesis being negatively associated with the premise;determining, by the processor, neutral entailment value from theconfidence index for each of the plurality of hypothesis, the neutralentailment value indicating a probability of a hypothesis from theplurality of hypothesis being neutrally associated with the premise;determining, by the processor, an entailed output index by collating theentailment value, the contradiction value, and the neutral entailmentvalue for each of the plurality of hypothesis; and generating, by theprocessor, an entailment result relevant for resolving the query, theentailment result comprising the plurality of hypothesis along with thecorresponding entailed output index.
 9. The method as claimed in claim8, wherein the knowledge database is a natural language data directory.10. The method as claimed in claim 8, wherein the method furthercomprises generating, by the processor, a premise graph and a hypothesisgraph, the premise graph mapping the first word data set against thesecond word data set, and the hypothesis graph mapping the second worddata set against the first word data set.
 11. The method as claimed inclaim 10, wherein the method further comprises generating, by theprocessor, the confidence index by comparing the premise graph and thehypothesis graph.
 12. The method as claimed in claim 8, wherein themethod further comprises implementing the second cognitive learningoperation for identifying a highest value amongst the entailment value,the contradiction value, and the neutral entailment value for each ofthe plurality of hypothesis.
 13. The method as claimed in claim 12,wherein the entailment result further including an entailment outputcorresponding to the highest value from the entailed output indexassociated with each of the plurality of hypothesis.
 14. The method asclaimed in claim 8, wherein the method further comprises establishing,by the processor, an entailment data library, by associating entailmentdata with the premise, the plurality of hypothesis and the confidenceindex for each of the plurality of hypothesis.
 15. A non-transitorycomputer readable medium including machine readable instructions thatare executable by a processor to: obtain a query from a user, the queryindicating a data entailment requirement comprising entailment data andassociated with entailment operations; implement an artificialintelligence component to: identify a word index from a knowledgedatabase, the word index including a plurality of words being associatedwith the data entailment requirement; identify a premise from theentailment data, the premise comprising a first word data set associatedwith the data entailment requirement; and determine a premise index bymapping the first word data set with the word index; implement a firstcognitive learning operation to determine a plurality of hypothesiscorresponding to the premise, each of the plurality of hypothesiscomprising a second word data set and indicating an inference associatedwith the premise, the second word data set being associated with theword index; determine a hypothesis index by mapping the second word dataset with the word index generate a confidence index for each of theplurality of hypothesis based on a comparison of the hypothesis indexwith the premise index; determine an entailment value based on theconfidence index for each of the plurality of hypothesis, the entailmentvalue indicating a probability of a hypothesis from the plurality ofhypothesis being positively associated with the premise; determine acontradiction value from the confidence index for each of the pluralityof hypothesis, the contradiction value indicating a probability of ahypothesis from the plurality of hypothesis being negatively associatedwith the premise; determine neutral entailment value from the confidenceindex for each of the plurality of hypothesis, the neutral entailmentvalue indicating a probability of a hypothesis from the plurality ofhypothesis being neutrally associated with the premise; determine anentailed output index by collating the entailment value, thecontradiction value, and the neutral entailment value for each of theplurality of hypothesis; and generate an entailment result relevant forresolving the query, the entailment result comprising the plurality ofhypothesis along with the corresponding entailed output index.
 16. Thenon-transitory computer-readable medium of claim 15, wherein theknowledge database is a natural language data directory.
 17. Thenon-transitory computer-readable medium of claim 15, wherein theprocessor is to generate a premise graph and a hypothesis graph, thepremise graph mapping the first word data set against the second worddata set, and the hypothesis graph mapping the second word data setagainst the first word data set.
 18. The non-transitorycomputer-readable medium of claim 17, wherein the processor is togenerate the confidence index by comparing the premise graph and thehypothesis graph.
 19. The non-transitory computer-readable medium ofclaim 15, wherein the processor is to implement the second cognitivelearning operation for identifying a highest value amongst theentailment value, the contradiction value, and the neutral entailmentvalue for each of the plurality of hypothesis.
 20. The non-transitorycomputer-readable medium of claim 19, wherein the entailment resultfurther including an entailment output corresponding to the highestvalue from the entailed output index associated with each of theplurality of hypothesis.